ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model.

The enzyme-linked immunosorbent spot (ELISpot) assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is used widely in the context of cancer and infectious diseases to validate the immunogenicity of predicted epitopes. While technological advances have kept pace with the demand for increased throughput, efforts to increase scale are bottlenecked by current assay design and deconvolution methods, which have remained largely unchanged. Current methods for designing pooled ELISpot experiments offer limited flexibility of assay parameters, lack support for high-throughput scenarios and do not consider peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles the deconvolution of positive peptides using assay readouts. In this study, we present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 model that groups immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays compared to existing combinatorial approaches. To validate ACE's performance on real-world datasets, we conducted a comprehensive benchmark study, contextualizing design choices with their impact on prediction quality. Our results demonstrate ACE's capacity to further increase precision of identified immunogenic peptides, directly optimizing experimental efficiency. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer.

Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response (

Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer.

BACKGROUND: Tertiary Lymphoid Structures (TLS) correlate with positive outcomes in patients with NSCLC and the efficacy of immune checkpoint blockade (ICB) in cancer. The actin regulatory protein hMENA undergoes tissue-specific splicing, producing the epithelial hMENA11a linked to favorable prognosis in early NSCLC, and the mesenchymal hMENAΔv6 found in invasive cancer cells and pro-tumoral cancer-associated fibroblasts (CAFs). This study investigates how hMENA isoforms in tumor cells and CAFs relate to TLS presence, localization and impact on patient outcomes and ICB response. METHODS: Methods involved RNA-SEQ on NSCLC cells with depleted hMENA isoforms. A retrospective observational study assessed tissues from surgically treated N0 patients with NSCLC, using immunohistochemistry for tumoral and stromal hMENA isoforms, fibronectin, and TLS presence. ICB-treated patient tumors were analyzed using Nanostring nCounter and GeoMx spatial transcriptomics. Multiparametric flow cytometry characterized B cells and tissue-resident memory T cells (TRM). Survival and ICB response were estimated in the cohort and validated using bioinformatics pipelines in different datasets. FINDINGS: Findings indicate that hMENA11a in NSCLC cells upregulates the TLS regulator LTßR, decreases fibronectin, and favors CXCL13 production by TRM. Conversely, hMENAΔv6 in CAFs inhibits LTßR-related NF-kB pathway, reduces CXCL13 secretion, and promotes fibronectin production. These patterns are validated in N0 NSCLC tumors, where hMENA11ahigh expression, CAF hMENAΔv6low, and stromal fibronectinlow are associated with intratumoral TLS, linked to memory B cells and predictive of longer survival. The hMENA isoform pattern, fibronectin, and LTßR expression broadly predict ICB response in tumors where TLS indicates an anti-tumor immune response. INTERPRETATION: This study uncovers hMENA alternative splicing as an unexplored contributor to TLS-related Tumor Immune Microenvironment (TIME) and a promising biomarker for clinical outcomes and likely ICB responsiveness in N0 patients with NSCLC. FUNDING: This work is supported by AIRC (IG 19822), ACC (RCR-2019-23669120), CAL.HUB.RIA Ministero Salute PNRR-POS T4, "Ricerca Corrente" granted by the Italian Ministry of Health.

Anti-CD30 CAR T cells as consolidation after autologous haematopoietic stem-cell transplantation in patients with high-risk CD30+ lymphoma: a phase 1 study.

BACKGROUND: Chimeric antigen receptor (CAR) T cells targeting CD30 are safe and have promising activity when preceded by lymphodepleting chemotherapy. We aimed to determine the safety of anti-CD30 CAR T cells as consolidation after autologous haematopoietic stem-cell transplantation (HSCT) in patients with CD30+ lymphoma at high risk of relapse. METHODS: This phase 1 dose-escalation study was performed at two sites in the USA. Patients aged 3 years and older, with classical Hodgkin lymphoma or non-Hodgkin lymphoma with CD30+ disease documented by immunohistochemistry, and a Karnofsky performance score of more than 60% planned for autologous HSCT were eligible if they were considered high risk for relapse as defined by primary refractory disease or relapse within 12 months of initial therapy or extranodal involvement at the start of pre-transplantation salvage therapy. Patients received a single infusion of CAR T cells (2 × 107 CAR T cells per m2, 1 × 108 CAR T cells per m2, or 2 × 108 CAR T cells per m2) as consolidation after trilineage haematopoietic engraftment (defined as absolute neutrophil count ≥500 cells per µL for 3 days, platelet count ≥25 × 109 platelets per L without transfusion for 5 days, and haemoglobin ≥8 g/dL without transfusion for 5 days) following carmustine, etoposide, cytarabine, and melphalan (BEAM) and HSCT. The primary endpoint was the determination of the maximum tolerated dose, which was based on the rate of dose-limiting toxicity in patients who received CAR T-cell infusion. This study is registered with ClinicalTrials.gov (NCT02663297) and enrolment is complete. FINDINGS: Between June 7, 2016, and Nov 30, 2020, 21 patients were enrolled and 18 patients (11 with Hodgkin lymphoma, six with T-cell lymphoma, one with grey zone lymphoma) were infused with anti-CD30 CAR T cells at a median of 22 days (range 16-44) after autologous HSCT. There were no dose-limiting toxicities observed, so the highest dose tested, 2 × 108 CAR T cells per m2, was determined to be the maximum tolerated dose. One patient had grade 1 cytokine release syndrome. The most common grade 3-4 adverse events were lymphopenia (two [11%] of 18) and leukopenia (two [11%] of 18). There were no treatment-related deaths. Two patients developed secondary malignancies approximately 2 years and 2·5 years following treatment (one stage 4 non-small cell lung cancer and one testicular cancer), but these were judged unrelated to treatment. At a median follow-up of 48·2 months (IQR 27·5-60·7) post-infusion, the median progression-free survival for all treated patients (n=18) was 32·3 months (95% CI 4·6 months to not estimable) and the median progression-free survival for treated patients with Hodgkin lymphoma (n=11) has not been reached. The median overall survival for all treated patients has not been reached. INTERPRETATION: Anti-CD30 CAR T-cell infusion as consolidation after BEAM and autologous HSCT is safe, with low rates of toxicity and encouraging preliminary activity in patients with Hodgkin lymphoma at high risk of relapse, highlighting the need for larger studies to confirm these findings. FUNDING: National Heart Lung and Blood Institute, University Cancer Research Fund at the Lineberger Comprehensive Cancer Center.

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.